Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f1b70168a58>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
celeba_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(celeba_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f1b700a5668>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.5.0
Default GPU Device: /device:GPU:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name="real_input")
    z_input = tf.placeholder(tf.float32, (None, z_dim), name="z_input")
    learning_rate = tf.placeholder(tf.float32, name="learning_rate")

    return real_input, z_input, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def leaky_relu(x, lrelu_alpha=0.2):
        return tf.maximum(lrelu_alpha * x, x)

def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    
    with tf.variable_scope("discriminator", reuse=reuse):
        # input is N*28*28*3
        # output = N*14*14*64
        layer1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',
                                 kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        layer1 = leaky_relu(layer1, alpha)
        
        # output = N*7*7*128
        layer2 = tf.layers.conv2d(layer1, 128, 5, strides=2, padding='same',
                                 kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        layer2 = tf.layers.batch_normalization(layer2, training=True)
        layer2 = leaky_relu(layer2, alpha)
        
        # output = N*4*4*256
        layer3 = tf.layers.conv2d(layer2, 256, 5, strides=2, padding='same',
                                 kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        layer3 = tf.layers.batch_normalization(layer3, training=True)
        layer3 = leaky_relu(layer3, alpha)
                
        flat = tf.layers.flatten(layer3)
        logits = tf.layers.dense(flat, 1,
                                )
        out = tf.sigmoid(logits)      

    return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2, reuse=None):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    local_reuse = (not is_train) if not reuse else reuse
    
    with tf.variable_scope("generator", reuse=local_reuse):
        
        # input: N*z_dim
        # output: 7*7*256
        layer1 = tf.layers.dense(z, 7*7*256, 
                                 kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        layer1 = tf.reshape(layer1, (-1, 7, 7, 256))
        layer1 = tf.layers.batch_normalization(layer1, training=is_train)
        layer1 = leaky_relu(layer1, alpha)
        
        # output: N*14*14*128
        layer2 = tf.layers.conv2d_transpose(layer1, 128, 5, strides=2, padding='same',
                                           kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        layer2 = tf.layers.batch_normalization(layer2, training=is_train)
        layer2 = leaky_relu(layer2, alpha)
        
        # output: N*28*28*64
        layer3 = tf.layers.conv2d_transpose(layer2, 64, 5, strides=2, padding='same',
                                           kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        layer3 = tf.layers.batch_normalization(layer3, training=is_train)
        layer3 = leaky_relu(layer3, alpha)     
        
        # output: N*28*28*3
        logits = tf.layers.conv2d_transpose(layer3, out_channel_dim, 5, strides=1, padding='same',
                                           kernel_initializer=tf.random_uniform_initializer(-0.01, 0.01))
        
        out = tf.tanh(logits)        
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """

    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """

    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    
    ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    ctrl = [op for op in ops if op.name.startswith('generator')]

    with tf.control_dependencies(ctrl):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt    
    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
class GAN:
    def __init__(self, real_size, z_size, learning_rate, alpha=0.2, beta1=0.5):

        self.input_real, self.input_z, learn_rate = model_inputs(*real_size,  z_size)
        self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z,
                                              real_size[2], alpha=alpha)    
        self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)
In [12]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1,
          get_batches, data_shape, data_image_mode, print_every=1, show_every=5):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """

    steps = 0
    e = 0
    samples, losses = [], []
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            e += 1
            for batch_images in get_batches(batch_size):
                steps += 1
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(net.d_opt, feed_dict={net.input_real: batch_images, net.input_z: batch_z})
                _ = sess.run(net.g_opt, feed_dict={net.input_z: batch_z, net.input_real: batch_images})
                
                if steps % print_every == 0:
                    train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: batch_images
                                                   })
                    train_loss_g = net.g_loss.eval({net.input_z: batch_z})
                    print("Epoch {}/{}...".format(e, epoch_count),
                          "Iteration {}".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    losses.append((train_loss_d, train_loss_g))   
                    
                    if steps % show_every == 0:
                        out_channel_dim = net.input_real.shape[-1]
                        image_mode = 'L' if out_channel_dim == 1 else 'RGB'
                        gen_samples = sess.run(
                                       generator(net.input_z, out_channel_dim, reuse=True, is_train=False),
                                       feed_dict={net.input_z: batch_z})
                        samples.append(gen_samples)
                        show_generator_output(sess, 36, net.input_z, out_channel_dim, image_mode)
              
In [13]:
device = tf.device('/gpu:0')

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002

real_size = (28, 28, 1)
alpha = 0.2
beta1 = 0.5

print_every = 10
show_every = 100


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))

tf.reset_default_graph()

with tf.Graph().as_default():
    net = GAN(real_size, z_dim, learning_rate, alpha=alpha, beta1=beta1)
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, 
          print_every=print_every, show_every=show_every)
Epoch 1/2... Iteration 10 Discriminator Loss: 1.2023... Generator Loss: 0.6469
Epoch 1/2... Iteration 20 Discriminator Loss: 1.6504... Generator Loss: 2.9328
Epoch 1/2... Iteration 30 Discriminator Loss: 0.9287... Generator Loss: 0.9584
Epoch 1/2... Iteration 40 Discriminator Loss: 0.9784... Generator Loss: 0.8485
Epoch 1/2... Iteration 50 Discriminator Loss: 1.1129... Generator Loss: 1.1381
Epoch 1/2... Iteration 60 Discriminator Loss: 1.0428... Generator Loss: 0.5472
Epoch 1/2... Iteration 70 Discriminator Loss: 1.0255... Generator Loss: 0.8305
Epoch 1/2... Iteration 80 Discriminator Loss: 0.9904... Generator Loss: 0.9262
Epoch 1/2... Iteration 90 Discriminator Loss: 1.0606... Generator Loss: 0.7102
Epoch 1/2... Iteration 100 Discriminator Loss: 1.1887... Generator Loss: 1.2313
Epoch 1/2... Iteration 110 Discriminator Loss: 1.2570... Generator Loss: 0.5436
Epoch 1/2... Iteration 120 Discriminator Loss: 0.8966... Generator Loss: 0.9445
Epoch 1/2... Iteration 130 Discriminator Loss: 1.1573... Generator Loss: 0.5483
Epoch 1/2... Iteration 140 Discriminator Loss: 1.6065... Generator Loss: 0.3118
Epoch 1/2... Iteration 150 Discriminator Loss: 1.3396... Generator Loss: 1.9468
Epoch 1/2... Iteration 160 Discriminator Loss: 0.9524... Generator Loss: 0.8750
Epoch 1/2... Iteration 170 Discriminator Loss: 0.9119... Generator Loss: 0.9268
Epoch 1/2... Iteration 180 Discriminator Loss: 0.7815... Generator Loss: 1.0341
Epoch 1/2... Iteration 190 Discriminator Loss: 0.8727... Generator Loss: 0.8444
Epoch 1/2... Iteration 200 Discriminator Loss: 0.9242... Generator Loss: 1.0919
Epoch 1/2... Iteration 210 Discriminator Loss: 1.4038... Generator Loss: 0.3553
Epoch 1/2... Iteration 220 Discriminator Loss: 0.7922... Generator Loss: 2.0279
Epoch 1/2... Iteration 230 Discriminator Loss: 0.7366... Generator Loss: 2.0581
Epoch 1/2... Iteration 240 Discriminator Loss: 0.7866... Generator Loss: 1.0429
Epoch 1/2... Iteration 250 Discriminator Loss: 0.7327... Generator Loss: 1.6286
Epoch 1/2... Iteration 260 Discriminator Loss: 0.6973... Generator Loss: 2.1056
Epoch 1/2... Iteration 270 Discriminator Loss: 0.8340... Generator Loss: 0.8060
Epoch 1/2... Iteration 280 Discriminator Loss: 0.9166... Generator Loss: 0.7695
Epoch 1/2... Iteration 290 Discriminator Loss: 0.7205... Generator Loss: 1.0351
Epoch 1/2... Iteration 300 Discriminator Loss: 0.6726... Generator Loss: 1.5178
Epoch 1/2... Iteration 310 Discriminator Loss: 1.3324... Generator Loss: 0.4395
Epoch 1/2... Iteration 320 Discriminator Loss: 1.0303... Generator Loss: 0.6328
Epoch 1/2... Iteration 330 Discriminator Loss: 1.2477... Generator Loss: 0.4236
Epoch 1/2... Iteration 340 Discriminator Loss: 0.6637... Generator Loss: 1.3377
Epoch 1/2... Iteration 350 Discriminator Loss: 1.1256... Generator Loss: 1.8628
Epoch 1/2... Iteration 360 Discriminator Loss: 0.7898... Generator Loss: 0.9007
Epoch 1/2... Iteration 370 Discriminator Loss: 1.2082... Generator Loss: 0.4672
Epoch 1/2... Iteration 380 Discriminator Loss: 0.3704... Generator Loss: 1.6767
Epoch 1/2... Iteration 390 Discriminator Loss: 0.8001... Generator Loss: 1.0278
Epoch 1/2... Iteration 400 Discriminator Loss: 2.7439... Generator Loss: 5.0970
Epoch 1/2... Iteration 410 Discriminator Loss: 0.5874... Generator Loss: 1.2048
Epoch 1/2... Iteration 420 Discriminator Loss: 0.9490... Generator Loss: 0.7101
Epoch 1/2... Iteration 430 Discriminator Loss: 1.0159... Generator Loss: 0.6440
Epoch 1/2... Iteration 440 Discriminator Loss: 1.0130... Generator Loss: 0.6401
Epoch 1/2... Iteration 450 Discriminator Loss: 0.5868... Generator Loss: 1.1484
Epoch 1/2... Iteration 460 Discriminator Loss: 0.4589... Generator Loss: 1.4745
Epoch 1/2... Iteration 470 Discriminator Loss: 1.5215... Generator Loss: 3.0330
Epoch 1/2... Iteration 480 Discriminator Loss: 0.8983... Generator Loss: 0.9618
Epoch 1/2... Iteration 490 Discriminator Loss: 1.0981... Generator Loss: 0.5425
Epoch 1/2... Iteration 500 Discriminator Loss: 0.5273... Generator Loss: 1.4202
Epoch 1/2... Iteration 510 Discriminator Loss: 0.4021... Generator Loss: 2.2361
Epoch 1/2... Iteration 520 Discriminator Loss: 0.4842... Generator Loss: 1.3749
Epoch 1/2... Iteration 530 Discriminator Loss: 1.0864... Generator Loss: 0.5349
Epoch 1/2... Iteration 540 Discriminator Loss: 0.8766... Generator Loss: 0.7451
Epoch 1/2... Iteration 550 Discriminator Loss: 1.0253... Generator Loss: 1.4397
Epoch 1/2... Iteration 560 Discriminator Loss: 0.6169... Generator Loss: 1.4771
Epoch 1/2... Iteration 570 Discriminator Loss: 0.2203... Generator Loss: 2.6575
Epoch 1/2... Iteration 580 Discriminator Loss: 0.5712... Generator Loss: 1.2178
Epoch 1/2... Iteration 590 Discriminator Loss: 0.2858... Generator Loss: 2.3312
Epoch 1/2... Iteration 600 Discriminator Loss: 0.6475... Generator Loss: 1.1093
Epoch 1/2... Iteration 610 Discriminator Loss: 0.8242... Generator Loss: 0.7407
Epoch 1/2... Iteration 620 Discriminator Loss: 0.3832... Generator Loss: 1.5005
Epoch 1/2... Iteration 630 Discriminator Loss: 0.1278... Generator Loss: 3.0289
Epoch 1/2... Iteration 640 Discriminator Loss: 0.3606... Generator Loss: 3.1161
Epoch 1/2... Iteration 650 Discriminator Loss: 0.1567... Generator Loss: 3.1022
Epoch 1/2... Iteration 660 Discriminator Loss: 0.2549... Generator Loss: 2.2402
Epoch 1/2... Iteration 670 Discriminator Loss: 1.7223... Generator Loss: 0.4938
Epoch 1/2... Iteration 680 Discriminator Loss: 0.6094... Generator Loss: 1.1751
Epoch 1/2... Iteration 690 Discriminator Loss: 0.4998... Generator Loss: 1.4544
Epoch 1/2... Iteration 700 Discriminator Loss: 0.3378... Generator Loss: 3.0787
Epoch 1/2... Iteration 710 Discriminator Loss: 0.8994... Generator Loss: 0.6821
Epoch 1/2... Iteration 720 Discriminator Loss: 1.4024... Generator Loss: 0.4580
Epoch 1/2... Iteration 730 Discriminator Loss: 0.6808... Generator Loss: 1.2096
Epoch 1/2... Iteration 740 Discriminator Loss: 0.6424... Generator Loss: 1.0624
Epoch 1/2... Iteration 750 Discriminator Loss: 0.1864... Generator Loss: 2.2785
Epoch 1/2... Iteration 760 Discriminator Loss: 0.8043... Generator Loss: 0.8746
Epoch 1/2... Iteration 770 Discriminator Loss: 0.2831... Generator Loss: 2.1750
Epoch 1/2... Iteration 780 Discriminator Loss: 0.0981... Generator Loss: 4.2875
Epoch 1/2... Iteration 790 Discriminator Loss: 0.1341... Generator Loss: 4.2802
Epoch 1/2... Iteration 800 Discriminator Loss: 0.7246... Generator Loss: 0.8920
Epoch 1/2... Iteration 810 Discriminator Loss: 0.3717... Generator Loss: 1.8557
Epoch 1/2... Iteration 820 Discriminator Loss: 0.5894... Generator Loss: 2.0594
Epoch 1/2... Iteration 830 Discriminator Loss: 1.1100... Generator Loss: 0.8177
Epoch 1/2... Iteration 840 Discriminator Loss: 0.6539... Generator Loss: 1.4902
Epoch 1/2... Iteration 850 Discriminator Loss: 0.6703... Generator Loss: 1.0040
Epoch 1/2... Iteration 860 Discriminator Loss: 0.3736... Generator Loss: 1.5953
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Epoch 2/2... Iteration 1230 Discriminator Loss: 0.6594... Generator Loss: 2.9078
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Epoch 2/2... Iteration 1250 Discriminator Loss: 0.1996... Generator Loss: 2.6511
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Epoch 2/2... Iteration 1710 Discriminator Loss: 0.2882... Generator Loss: 2.2134
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Epoch 2/2... Iteration 1790 Discriminator Loss: 0.2569... Generator Loss: 2.0725
Epoch 2/2... Iteration 1800 Discriminator Loss: 0.5172... Generator Loss: 1.2200
Epoch 2/2... Iteration 1810 Discriminator Loss: 0.6959... Generator Loss: 5.8156
Epoch 2/2... Iteration 1820 Discriminator Loss: 0.9526... Generator Loss: 1.5883
Epoch 2/2... Iteration 1830 Discriminator Loss: 0.9106... Generator Loss: 1.3727
Epoch 2/2... Iteration 1840 Discriminator Loss: 0.8706... Generator Loss: 0.7660
Epoch 2/2... Iteration 1850 Discriminator Loss: 0.5150... Generator Loss: 1.7011
Epoch 2/2... Iteration 1860 Discriminator Loss: 0.7234... Generator Loss: 1.2444
Epoch 2/2... Iteration 1870 Discriminator Loss: 1.2717... Generator Loss: 0.5647

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002

real_size = (28, 28, 3)
epochs = 25
alpha = 0.2
beta1 = 0.5

print_every = 10
show_every = 100


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    net = GAN(real_size, z_dim, learning_rate, alpha=alpha, beta1=beta1)
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode,
          print_every=print_every, show_every=show_every)
Epoch 1/1... Iteration 10 Discriminator Loss: 0.9407... Generator Loss: 0.6128
Epoch 1/1... Iteration 20 Discriminator Loss: 2.0005... Generator Loss: 0.2202
Epoch 1/1... Iteration 30 Discriminator Loss: 1.4384... Generator Loss: 0.7736
Epoch 1/1... Iteration 40 Discriminator Loss: 1.5430... Generator Loss: 2.2678
Epoch 1/1... Iteration 50 Discriminator Loss: 1.1389... Generator Loss: 1.2046
Epoch 1/1... Iteration 60 Discriminator Loss: 1.1053... Generator Loss: 0.9186
Epoch 1/1... Iteration 70 Discriminator Loss: 1.6353... Generator Loss: 0.3342
Epoch 1/1... Iteration 80 Discriminator Loss: 1.0722... Generator Loss: 0.7950
Epoch 1/1... Iteration 90 Discriminator Loss: 1.3334... Generator Loss: 0.6622
Epoch 1/1... Iteration 100 Discriminator Loss: 1.5694... Generator Loss: 0.6693
Epoch 1/1... Iteration 110 Discriminator Loss: 1.3045... Generator Loss: 1.5941
Epoch 1/1... Iteration 120 Discriminator Loss: 1.5075... Generator Loss: 0.5144
Epoch 1/1... Iteration 130 Discriminator Loss: 1.4900... Generator Loss: 0.7995
Epoch 1/1... Iteration 140 Discriminator Loss: 1.3032... Generator Loss: 0.7818
Epoch 1/1... Iteration 150 Discriminator Loss: 1.2110... Generator Loss: 0.9671
Epoch 1/1... Iteration 160 Discriminator Loss: 1.5673... Generator Loss: 1.3082
Epoch 1/1... Iteration 170 Discriminator Loss: 1.5059... Generator Loss: 0.5460
Epoch 1/1... Iteration 180 Discriminator Loss: 1.2455... Generator Loss: 0.6365
Epoch 1/1... Iteration 190 Discriminator Loss: 1.6605... Generator Loss: 0.5831
Epoch 1/1... Iteration 200 Discriminator Loss: 1.4224... Generator Loss: 0.6113
Epoch 1/1... Iteration 210 Discriminator Loss: 1.6304... Generator Loss: 0.5815
Epoch 1/1... Iteration 220 Discriminator Loss: 1.0432... Generator Loss: 0.8373
Epoch 1/1... Iteration 230 Discriminator Loss: 1.4740... Generator Loss: 0.6673
Epoch 1/1... Iteration 240 Discriminator Loss: 2.5603... Generator Loss: 0.6923
Epoch 1/1... Iteration 250 Discriminator Loss: 1.0641... Generator Loss: 0.8655
Epoch 1/1... Iteration 260 Discriminator Loss: 1.2163... Generator Loss: 0.7767
Epoch 1/1... Iteration 270 Discriminator Loss: 1.3097... Generator Loss: 0.6640
Epoch 1/1... Iteration 280 Discriminator Loss: 1.3170... Generator Loss: 0.6223
Epoch 1/1... Iteration 290 Discriminator Loss: 1.0853... Generator Loss: 0.7990
Epoch 1/1... Iteration 300 Discriminator Loss: 1.5109... Generator Loss: 1.3188
Epoch 1/1... Iteration 310 Discriminator Loss: 1.3224... Generator Loss: 0.7338
Epoch 1/1... Iteration 320 Discriminator Loss: 1.3132... Generator Loss: 0.7634
Epoch 1/1... Iteration 330 Discriminator Loss: 1.1499... Generator Loss: 0.6364
Epoch 1/1... Iteration 340 Discriminator Loss: 1.1996... Generator Loss: 0.8127
Epoch 1/1... Iteration 350 Discriminator Loss: 1.3929... Generator Loss: 0.6643
Epoch 1/1... Iteration 360 Discriminator Loss: 1.4387... Generator Loss: 0.5595
Epoch 1/1... Iteration 370 Discriminator Loss: 1.3160... Generator Loss: 0.6232
Epoch 1/1... Iteration 380 Discriminator Loss: 1.6473... Generator Loss: 0.3567
Epoch 1/1... Iteration 390 Discriminator Loss: 1.1894... Generator Loss: 0.6556
Epoch 1/1... Iteration 400 Discriminator Loss: 1.4177... Generator Loss: 0.7615
Epoch 1/1... Iteration 410 Discriminator Loss: 2.0667... Generator Loss: 0.2282
Epoch 1/1... Iteration 420 Discriminator Loss: 1.1503... Generator Loss: 0.8968
Epoch 1/1... Iteration 430 Discriminator Loss: 1.4037... Generator Loss: 0.7111
Epoch 1/1... Iteration 440 Discriminator Loss: 1.1791... Generator Loss: 0.7648
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Epoch 1/1... Iteration 870 Discriminator Loss: 1.3083... Generator Loss: 0.8193
Epoch 1/1... Iteration 880 Discriminator Loss: 1.2551... Generator Loss: 0.7292
Epoch 1/1... Iteration 890 Discriminator Loss: 1.2319... Generator Loss: 0.8030
Epoch 1/1... Iteration 900 Discriminator Loss: 1.1954... Generator Loss: 0.7830
Epoch 1/1... Iteration 910 Discriminator Loss: 1.3789... Generator Loss: 0.6703
Epoch 1/1... Iteration 920 Discriminator Loss: 1.2164... Generator Loss: 1.5326
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Epoch 1/1... Iteration 970 Discriminator Loss: 0.9507... Generator Loss: 1.4916
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Epoch 1/1... Iteration 990 Discriminator Loss: 0.9808... Generator Loss: 0.9149
Epoch 1/1... Iteration 1000 Discriminator Loss: 1.0456... Generator Loss: 1.3696
Epoch 1/1... Iteration 1010 Discriminator Loss: 1.2680... Generator Loss: 1.3969
Epoch 1/1... Iteration 1020 Discriminator Loss: 1.1330... Generator Loss: 2.9837
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Epoch 1/1... Iteration 1040 Discriminator Loss: 1.0632... Generator Loss: 0.9797
Epoch 1/1... Iteration 1050 Discriminator Loss: 0.7832... Generator Loss: 1.2238
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Epoch 1/1... Iteration 1070 Discriminator Loss: 0.9416... Generator Loss: 1.2433
Epoch 1/1... Iteration 1080 Discriminator Loss: 1.2028... Generator Loss: 1.6876
Epoch 1/1... Iteration 1090 Discriminator Loss: 0.7993... Generator Loss: 1.3788
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Epoch 1/1... Iteration 1130 Discriminator Loss: 1.2938... Generator Loss: 1.2049
Epoch 1/1... Iteration 1140 Discriminator Loss: 1.1796... Generator Loss: 0.7064
Epoch 1/1... Iteration 1150 Discriminator Loss: 1.1875... Generator Loss: 0.7695
Epoch 1/1... Iteration 1160 Discriminator Loss: 1.0202... Generator Loss: 1.6274
Epoch 1/1... Iteration 1170 Discriminator Loss: 0.9936... Generator Loss: 0.7287
Epoch 1/1... Iteration 1180 Discriminator Loss: 1.1239... Generator Loss: 0.7434
Epoch 1/1... Iteration 1190 Discriminator Loss: 1.3263... Generator Loss: 1.3005
Epoch 1/1... Iteration 1200 Discriminator Loss: 1.2998... Generator Loss: 0.6474
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Epoch 1/1... Iteration 1260 Discriminator Loss: 0.8586... Generator Loss: 1.2965
Epoch 1/1... Iteration 1270 Discriminator Loss: 1.1760... Generator Loss: 0.5327
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Epoch 1/1... Iteration 1290 Discriminator Loss: 1.3040... Generator Loss: 0.4386
Epoch 1/1... Iteration 1300 Discriminator Loss: 1.3355... Generator Loss: 0.4762
Epoch 1/1... Iteration 1310 Discriminator Loss: 1.0420... Generator Loss: 1.0521
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Epoch 1/1... Iteration 1330 Discriminator Loss: 0.9632... Generator Loss: 0.8904
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Epoch 1/1... Iteration 1430 Discriminator Loss: 1.3248... Generator Loss: 0.5786
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Epoch 1/1... Iteration 1500 Discriminator Loss: 1.0604... Generator Loss: 1.0077
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Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.